An important decision to make when working with manipulators is the type of policy representation and on which level it interfaces with the robot. The latter can strongly influence the learning speed and the quality of the obtained solutions [1]}, [2]}. These choices also influence the form of priors that can be defined and how they are defined [3]}.
Not many policies combine the aforementioned properties of being a) interpretable, b) paramterizable for the task at hand and c) allow learning or improvement.
